Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification

Adjorlolo, Clement; Mutanga, Onisimo; Cho, Moses Azong; Ismail, Riyad

doi:10.1016/j.jag.2012.07.011

Cited by 37 publications

(33 citation statements)

References 48 publications

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“…The spectral regions of 350-399 nm, 1355-1420 nm, 1810-1940 nm and 2470-2500 nm (Figure 2) are known to be noisy and were discarded from the spectra. 5,39 Analysis of variance and Brown-Forsythe tests…”

Section: Discussionmentioning

confidence: 99%

Potential of interval partial least square regression in estimating leaf area index

Kiala¹,

Odindi²,

Mutanga³

2017

S. Afr. J. Sci.

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ARTICLE INCLUDES:× Supplementary material × Data set FUNDING:None Leaf area index (LAI) is a critical parameter in determining vegetation status and health. In tropical grasslands, reliable determination of LAI, useful in determining above ground biomass, provides a basis for rangeland management, conservation and restoration. In this study, interval partial least square regression (iPLSR) in forward mode was compared to partial least square regression (PLSR) to estimate LAI from in-situ canopy hyperspectral data on a heterogeneous grassland at different periods (onset, mid and end) during summer. The performance of the two techniques was determined using the least relative root mean square error to the mean (nRMSEP) and the highest coefficients of determination (R 2 p ) between the predicted and the measured LAI. Results show that iPLSR models could explain LAI variation with R 2 p values ranging from 0.81 to 0.93 and low nRMSEP from 9.39% to 24.71%. The highest accuracies for estimates of LAI using iPLSR were at mid-and end of summer (R 2 p = 0.93 and nRMSEP = 9.39%; R 2 p = 0.89 and nRMSEP = 10.50%, respectively). Pooling data sets from the three assessed periods yielded the highest prediction error (nRMSEP=24.71%). Results show that iPLSR performed better than PLSR, which yielded R 2 pand RMSEP values ranging from 0.36 to 0.65 and from 28.44% to 33.47%, respectively. Overall, this study demonstrates the value of iPLSR in predicting LAI and therefore provides a basis for more accurate mapping and monitoring of canopy characteristics of tropical grasslands. Significance:• The relationship between LAI and canopy reflectance can be used in iPLSR modelling to provide more accurate mapping and monitoring of canopy characteristics for land management and conservation.

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Section: Discussionmentioning

confidence: 99%

Potential of interval partial least square regression in estimating leaf area index

Kiala¹,

Odindi²,

Mutanga³

2017

S. Afr. J. Sci.

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“…Many researchers have used random forest as a dimensionality reduction tool in different hyperspectral remote sensing applications [32,58,[77][78][79]. However, studies have shown drawbacks to the use of random forest as a tool to measure variable importance, as well as a variable selection method [68,80]. Therefore, in this study, we introduced a newly-developed method, which has never been tested before in hyperspectral variables selection.…”

Section: Discussionmentioning

confidence: 99%

“…The visible region of the spectrum is greatly affected by the selective absorption of the photosynthetic pigments [83]. The red edge region is the region in which the effect of vegetation biochemicals is most relevant [68]. The short-wave infrared (SWIR) is affected by water properties associated with vegetation, such as Leaf Area Index, strong leaf or canopy liquid water absorption and macronutrients [83][84][85].…”

Section: Discussionmentioning

confidence: 99%

“…However, these studies have shown that although RF provides insight into the importance of each variable in the classification process, it fails to automatically select the key number of variables that could yield the lowest error rate [31]. Moreover, studies have shown that random forest's preference for a highly correlated predictor variable in identifying variables in high-dimensional spectral space has been identified as its major limitation [44,68]. To address this shortcoming, a regularization framework that can be applied to random forest (regularized random forest) and boosted trees (regularized boosted trees) was developed by Deng and Runger [47].…”

Section: Feature Selection Using Guided Regularized Random Forestmentioning

confidence: 99%

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Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest

et al. 2016

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Abstract:The invasive taxa of Prosopis is rated the world's top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data with a newly-developed algorithm, guided regularized random forest (GRRF), to spectrally discriminate Prosopis from coexistent acacia species (Acacia karroo, Acacia mellifera and Ziziphus mucronata) in the arid environment of South Africa. Results show that GRRF was able to reduce the high dimensionality of the spectroscopy data and select key wavelengths (n = 11) for discriminating amongst the species. These wavelengths are located at 356.3 nm, 468.5 nm, 531.1 nm, 665.2 nm, 1262.3 nm, 1354.1 nm, 1361.7 nm, 1376.9 nm, 1407.1 nm, 1410.9 nm and 1414.6 nm. The use of these selected wavelengths increases the overall classification accuracy from 79.19% and a Kappa value of 0.7201 when using all wavelengths to 88.59% and a Kappa of 0.8524 when the selected wavelengths were used. Based on our relatively high accuracies and ease of use, it is worth considering the GRRF method for reducing the high dimensionality of spectroscopy data. However, this assertion should receive considerable additional testing and comparison before it is accepted as a substitute for reliable high dimensionality reduction.

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“…However, its preference to highly correlated predictor variable in identifying variables in high-dimensional spectral space has been identified as its major limitation [38,39]. Moreover, while RF only provides insight into the importance of each variable in classification process, it does not automatically select the optimal number of variables that could yield the lowest error rate [40].…”

Section: Feature Selection Via Guided Regularized Random Forestmentioning

confidence: 99%

Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm

Adam

Deng

Odindi

et al. 2017

Journal of Spectroscopy

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Phaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize. Field data were collected from healthy and the early stage of PLS over two years (2013 and 2014) using a handheld spectroradiometer. An integration of a newly developed guided regularized random forest (GRRF) and a traditional random forest (RF) was used for feature selection and classification, respectively. The 2013 dataset was used to train the model, while the 2014 dataset was used as independent test dataset. Results showed that there were statistically significant differences in biochemical concentration between the healthy leaves and leaves that were at an early stage of PLS infestation. The newly developed GRRF was able to reduce the high dimensionality of hyperspectral data by selecting key wavelengths with less autocorrelation. These wavelengths are located at 420 nm, 795 nm, 779 nm, 1543 nm, 1747 nm, and 1010 nm. Using these variables (n = 6), a random forest classifier was able to discriminate between the healthy maize and maize at an early stage of PLS infestation with an overall accuracy of 88% and a kappa value of 0.75. Overall, our study showed potential application of hyperspectral data, GRRF feature selection, and RF classifiers in detecting the early stage of PLS infestation in tropical maize.

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Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification

Cited by 37 publications

References 48 publications

Potential of interval partial least square regression in estimating leaf area index

Potential of interval partial least square regression in estimating leaf area index

Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest

Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm

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